Scaffolded LLMs as natural language computers
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However this prompts the question of why we are striving to create massive natural language models (with all the disadvantages of natural language) that we will then heavily constrain to perform tasks that can be performed by traditional computers with order-of-magnitude greater efficiency and reliability? Most of these "chaining" libraries are already engaged in asking the LLM to pretty-please output a standardized blob format that can be read by a Python harness, where we use the LLM to identify that something is e.g. an equation, and then pack it off to Wolfram or something. It seems like if you want to do this more than a few times, it's better to write a couple lines of code to do it much more cheaply.
I could have done that with a person, but then I'd need someone who was available then and there that I wasn't taking away from other stuff.
To tie that to your points: This process was exactly an exercise in nailing down details that were missing because the short prose version was leaving huge gaps.
I intend to need a back and forth of filling in detail each step toward an implementation for that reason.
And I think this kind of tooling need to be built with that in mind: Write roughly what you need unless you already know how you want to express it in code. Ask for clarifications or a proposed plan. Iterate. Maybe with tooling giving examples of where it would head if prompted to fill in more detail.
I sure as hell won't trust it to just blindly do a task for me from a brief problem statement at this point.
Just as I wouldn't for most non-trivial tasks with most humans...
> It seems like if you want to do this more than a few times, it's better to write a couple lines of code to do it much more cheaply
I think the problem is that natively written code will always suck at the planning part. An LLM can use the tools available to it (eg a web browser or Wolfram) in infinite ways, and hopefully in a way that will advance the task at hand. Maybe the natively written task primitives can become really big (eg a fully automatic web scraper) - great, now the LLM can gather info for its plans even faster.
I agree. I think people are fooled by the implicit knowledge that most humans have. They make decisions about the details, where in programming you have to tend to each detail. That's roughly equivalent effort in natural language.
For some problems, probably harder in natural language.
I still think it's a great advancement. It opens programming to the masses, and the LLM can probably teach you the things you need to know to advance your competency.
That’s more expensive than writing a couple of lines of code, more than deploying it,
Sufficiently standardized, it will be significantly less expensive than paying someone to write a couple of lines of code, test it, deploy it, etc.
Is this better than just writing a couple of lines of code? That’s a different question. At scale, this can absolutely be cheaper. Eventually.
I’ve played with AutoGPT today, and, while the results were underwhelming (once it crashed, and once it got stuck in an infinite loop because it wrongly requested a website critical for the task) - it was an experience very similar to my first attempts at learning C. I tried to tell the system what I want it to do, and it mostly really followed my instructions. If (when?) all the components have improved in reliability and speed, this will become an insanely powerful way of working. A la „Make a website from this PDF with nextjs and deploy it to netlify“. Not very unlike „Read this file from disk and parse CSV rows from it“ as we are doing now, as devs working with high level programming languages.
GPT-3 to GPT-4 was an exponential improvement? Progress is not usually exponential. Your phone now isn’t x10 better than it was a few years ago. Progress in AI is a huge jump, then refinement of that jump, then stagnation until the next big jump is discovered. Look at CNNs dominating image classification competitions out of nowhere, then they got refined, then they kind of stagnated and didn’t get much % accuracy better in whatever benchmark was used, in fact I think humans are still way better at many vision tasks and it’s been over a decade of research now since CNNs jumped on the scene as the hot thing. I don’t know why people refuse to understand or see this, but it’s tiring constantly hearing people pretend everything is exponential and AGI is two years away when AI hasn’t even beaten humans at some pretty trivial vision benchmarks.
But--and this is a big but--the set of things that people can do better than computers has shrunk significantly over the past five months. Today, thanks to GPT4, AI can get a B in a Quantum Computing class, generate engaging stories, know that the color yellow is closer to orange than blue (despite never having seen color!), answer emotionally laden questions with the sort of facility that IMO is better than most humans, write code, rhyme, and much more. All of this stuff was unthinkable before. I personally thought it would be centuries until this stuff was possible. I was very wrong.
Several developments in Deep Learning, like the Transformers paper (https://arxiv.org/abs/1706.03762), set off this growth, as did big data and increased computing power. The insight of particular humans, like Ilya Sutskever, played a role as well. But taken together, I actually don't understand how one can argue that we aren't at the beginning of a massive exponential.
Of course there are things humans can still do better than AI, but the number of things is shrinking rapidly, while the number of things computers can do better than humans is growing rapidly.
I argue that we are indeed at the beginning of an exponential, and we'll see both new classes of products and faster development time.
Opened my mind to what is to come and how it might happen
Can we dive into the idea of "semantic error correcting codes" in this thread please?
this was another one:
https://www.amazon.com/Artificial-Intelligence-3rd-Winston/d...
On the topic of summarizing and determinism, I wonder if an intermediate bytecode loke language or structured subset of the English language could improve the outcome across models.
Those 2 chatbots could be of different models. Would they then teach each other something new?
Pretty incredible